Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations58,895
Missing cells126,083
Missing cells (%)6.5%
Duplicate rows3,822
Duplicate rows (%)6.5%
Total size in memory14.8 MiB
Average record size in memory264.0 B

Variable types

Categorical15
Numeric15
Text1
Unsupported1
DateTime1

Alerts

Dataset has 3822 (6.5%) duplicate rowsDuplicates
children is highly imbalanced (79.9%) Imbalance
meal is highly imbalanced (53.5%) Imbalance
distribution_channel is highly imbalanced (59.7%) Imbalance
is_repeated_guest is highly imbalanced (80.5%) Imbalance
reserved_room_type is highly imbalanced (51.4%) Imbalance
deposit_type is highly imbalanced (70.6%) Imbalance
required_car_parking_spaces is highly imbalanced (80.2%) Imbalance
agent has 9132 (15.5%) missing values Missing
company has 55416 (94.1%) missing values Missing
customer_type has 589 (1.0%) missing values Missing
required_car_parking_spaces has 589 (1.0%) missing values Missing
reservation_status has 589 (1.0%) missing values Missing
kids has 58694 (99.7%) missing values Missing
adults is highly skewed (γ1 = 24.81617275) Skewed
babies is highly skewed (γ1 = 25.35395751) Skewed
previous_cancellations is highly skewed (γ1 = 21.14836957) Skewed
company is an unsupported type, check if it needs cleaning or further analysis Unsupported
lead_time has 3700 (6.3%) zeros Zeros
stays_in_weekend_nights has 23496 (39.9%) zeros Zeros
stays_in_week_nights has 3603 (6.1%) zeros Zeros
babies has 58094 (98.6%) zeros Zeros
previous_cancellations has 57800 (98.1%) zeros Zeros
previous_bookings_not_canceled has 56863 (96.5%) zeros Zeros
booking_changes has 49268 (83.7%) zeros Zeros
days_in_waiting_list has 56503 (95.9%) zeros Zeros
adr has 954 (1.6%) zeros Zeros
total_of_special_requests has 37158 (63.1%) zeros Zeros

Reproduction

Analysis started2025-09-21 06:35:16.139197
Analysis finished2025-09-21 06:35:43.501166
Duration27.36 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
Resort Hotel
40063 
City Hotel
18832 

Length

Max length12
Median length12
Mean length11.360489
Min length10

Characters and Unicode

Total characters669,076
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
Resort Hotel 40063
68.0%
City Hotel 18832
32.0%

Length

2025-09-21T01:35:43.585664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:43.682702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hotel 58895
50.0%
resort 40063
34.0%
city 18832
 
16.0%

Most occurring characters

ValueCountFrequency (%)
t 117790
17.6%
e 98958
14.8%
o 98958
14.8%
58895
8.8%
H 58895
8.8%
l 58895
8.8%
R 40063
 
6.0%
s 40063
 
6.0%
r 40063
 
6.0%
C 18832
 
2.8%
Other values (2) 37664
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 669076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 117790
17.6%
e 98958
14.8%
o 98958
14.8%
58895
8.8%
H 58895
8.8%
l 58895
8.8%
R 40063
 
6.0%
s 40063
 
6.0%
r 40063
 
6.0%
C 18832
 
2.8%
Other values (2) 37664
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 669076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 117790
17.6%
e 98958
14.8%
o 98958
14.8%
58895
8.8%
H 58895
8.8%
l 58895
8.8%
R 40063
 
6.0%
s 40063
 
6.0%
r 40063
 
6.0%
C 18832
 
2.8%
Other values (2) 37664
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 669076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 117790
17.6%
e 98958
14.8%
o 98958
14.8%
58895
8.8%
H 58895
8.8%
l 58895
8.8%
R 40063
 
6.0%
s 40063
 
6.0%
r 40063
 
6.0%
C 18832
 
2.8%
Other values (2) 37664
 
5.6%

is_canceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
0
34666 
1
24229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58,895
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

Length

2025-09-21T01:35:43.751279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:43.834928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

Most occurring characters

ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34666
58.9%
1 24229
41.1%

lead_time
Real number (ℝ)

Zeros 

Distinct428
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0509
Minimum0
Maximum737
Zeros3700
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:43.919036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median69
Q3157
95-th percentile309
Maximum737
Range737
Interquartile range (IQR)140

Descriptive statistics

Standard deviation101.16242
Coefficient of variation (CV)1.0111095
Kurtosis1.0087856
Mean100.0509
Median Absolute Deviation (MAD)61
Skewness1.2082573
Sum5892498
Variance10233.835
MonotonicityNot monotonic
2025-09-21T01:35:44.018943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3700
 
6.3%
1 1923
 
3.3%
2 1106
 
1.9%
3 941
 
1.6%
4 830
 
1.4%
5 745
 
1.3%
7 687
 
1.2%
6 672
 
1.1%
12 527
 
0.9%
10 519
 
0.9%
Other values (418) 47245
80.2%
ValueCountFrequency (%)
0 3700
6.3%
1 1923
3.3%
2 1106
 
1.9%
3 941
 
1.6%
4 830
 
1.4%
5 745
 
1.3%
6 672
 
1.1%
7 687
 
1.2%
8 503
 
0.9%
9 477
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
605 9
 
< 0.1%
542 23
< 0.1%
532 1
 
< 0.1%
471 6
 
< 0.1%
468 47
0.1%
462 20
< 0.1%
461 32
0.1%
460 3
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing588
Missing (%)1.0%
Memory size460.2 KiB
2016.0
30105 
2015.0
14537 
2017.0
13051 
20016.0
 
614

Length

Max length7
Median length6
Mean length6.0105305
Min length6

Characters and Unicode

Total characters350,456
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015.0
2nd row2015.0
3rd row2015.0
4th row2015.0
5th row2015.0

Common Values

ValueCountFrequency (%)
2016.0 30105
51.1%
2015.0 14537
24.7%
2017.0 13051
22.2%
20016.0 614
 
1.0%
(Missing) 588
 
1.0%

Length

2025-09-21T01:35:44.120170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:44.201555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2016.0 30105
51.6%
2015.0 14537
24.9%
2017.0 13051
22.4%
20016.0 614
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 117228
33.5%
2 58307
16.6%
1 58307
16.6%
. 58307
16.6%
6 30719
 
8.8%
5 14537
 
4.1%
7 13051
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 350456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 117228
33.5%
2 58307
16.6%
1 58307
16.6%
. 58307
16.6%
6 30719
 
8.8%
5 14537
 
4.1%
7 13051
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 350456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 117228
33.5%
2 58307
16.6%
1 58307
16.6%
. 58307
16.6%
6 30719
 
8.8%
5 14537
 
4.1%
7 13051
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 350456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 117228
33.5%
2 58307
16.6%
1 58307
16.6%
. 58307
16.6%
6 30719
 
8.8%
5 14537
 
4.1%
7 13051
 
3.7%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
August
7715 
September
6712 
July
6177 
October
6040 
May
5283 
Other values (7)
26968 

Length

Max length9
Median length7
Mean length6.0207148
Min length3

Characters and Unicode

Total characters354,590
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 7715
13.1%
September 6712
11.4%
July 6177
10.5%
October 6040
10.3%
May 5283
9.0%
April 5185
8.8%
June 4725
8.0%
March 4492
7.6%
February 3830
6.5%
December 3121
5.3%
Other values (2) 5615
9.5%

Length

2025-09-21T01:35:44.285242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 7715
13.1%
september 6712
11.4%
july 6177
10.5%
october 6040
10.3%
may 5283
9.0%
april 5185
8.8%
june 4725
8.0%
march 4492
7.6%
february 3830
6.5%
december 3121
5.3%
Other values (2) 5615
9.5%

Most occurring characters

ValueCountFrequency (%)
e 49808
14.0%
r 38825
 
10.9%
u 32920
 
9.3%
b 22560
 
6.4%
t 20467
 
5.8%
a 19121
 
5.4%
y 18048
 
5.1%
J 13660
 
3.9%
c 13653
 
3.9%
A 12900
 
3.6%
Other values (16) 112628
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49808
14.0%
r 38825
 
10.9%
u 32920
 
9.3%
b 22560
 
6.4%
t 20467
 
5.8%
a 19121
 
5.4%
y 18048
 
5.1%
J 13660
 
3.9%
c 13653
 
3.9%
A 12900
 
3.6%
Other values (16) 112628
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49808
14.0%
r 38825
 
10.9%
u 32920
 
9.3%
b 22560
 
6.4%
t 20467
 
5.8%
a 19121
 
5.4%
y 18048
 
5.1%
J 13660
 
3.9%
c 13653
 
3.9%
A 12900
 
3.6%
Other values (16) 112628
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49808
14.0%
r 38825
 
10.9%
u 32920
 
9.3%
b 22560
 
6.4%
t 20467
 
5.8%
a 19121
 
5.4%
y 18048
 
5.1%
J 13660
 
3.9%
c 13653
 
3.9%
A 12900
 
3.6%
Other values (16) 112628
31.8%

arrival_date_week_number
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.837389
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:44.384779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.346053
Coefficient of variation (CV)0.47942904
Kurtosis-0.94526161
Mean27.837389
Median Absolute Deviation (MAD)11
Skewness-0.13277996
Sum1639483
Variance178.11712
MonotonicityNot monotonic
2025-09-21T01:35:44.484982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 2023
 
3.4%
34 1703
 
2.9%
41 1668
 
2.8%
38 1663
 
2.8%
32 1620
 
2.8%
42 1617
 
2.7%
37 1555
 
2.6%
40 1513
 
2.6%
35 1504
 
2.6%
30 1496
 
2.5%
Other values (43) 42533
72.2%
ValueCountFrequency (%)
1 402
 
0.7%
2 573
1.0%
3 675
1.1%
4 687
1.2%
5 591
1.0%
6 802
1.4%
7 1073
1.8%
8 882
1.5%
9 950
1.6%
10 996
1.7%
ValueCountFrequency (%)
53 807
1.4%
52 655
1.1%
51 467
0.8%
50 508
0.9%
49 844
1.4%
48 710
1.2%
47 814
1.4%
46 548
0.9%
45 750
1.3%
44 1000
1.7%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.766432
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:44.585117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7830365
Coefficient of variation (CV)0.55707192
Kurtosis-1.1763513
Mean15.766432
Median Absolute Deviation (MAD)8
Skewness0.021266696
Sum928564
Variance77.14173
MonotonicityNot monotonic
2025-09-21T01:35:44.684796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 2253
 
3.8%
12 2230
 
3.8%
16 2211
 
3.8%
18 2118
 
3.6%
17 2117
 
3.6%
30 2112
 
3.6%
26 2096
 
3.6%
9 2092
 
3.6%
15 2030
 
3.4%
25 2020
 
3.4%
Other values (21) 37616
63.9%
ValueCountFrequency (%)
1 1750
3.0%
2 1997
3.4%
3 1850
3.1%
4 1850
3.1%
5 2253
3.8%
6 1782
3.0%
7 1846
3.1%
8 1908
3.2%
9 2092
3.6%
10 1718
2.9%
ValueCountFrequency (%)
31 1186
2.0%
30 2112
3.6%
29 1712
2.9%
28 1820
3.1%
27 1711
2.9%
26 2096
3.6%
25 2020
3.4%
24 1978
3.4%
23 1767
3.0%
22 1810
3.1%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0581543
Minimum0
Maximum19
Zeros23496
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:44.768905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0930323
Coefficient of variation (CV)1.0329612
Kurtosis7.4681056
Mean1.0581543
Median Absolute Deviation (MAD)1
Skewness1.4335048
Sum62320
Variance1.1947197
MonotonicityNot monotonic
2025-09-21T01:35:44.834607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 23496
39.9%
2 18437
31.3%
1 14032
23.8%
4 1640
 
2.8%
3 1026
 
1.7%
6 128
 
0.2%
5 51
 
0.1%
8 42
 
0.1%
7 18
 
< 0.1%
9 8
 
< 0.1%
Other values (7) 17
 
< 0.1%
ValueCountFrequency (%)
0 23496
39.9%
1 14032
23.8%
2 18437
31.3%
3 1026
 
1.7%
4 1640
 
2.8%
5 51
 
0.1%
6 128
 
0.2%
7 18
 
< 0.1%
8 42
 
0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
10 5
 
< 0.1%
9 8
 
< 0.1%
8 42
0.1%
7 18
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8475762
Minimum0
Maximum50
Zeros3603
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:44.935098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2354858
Coefficient of variation (CV)0.78504864
Kurtosis18.796619
Mean2.8475762
Median Absolute Deviation (MAD)1
Skewness2.5680997
Sum167708
Variance4.9973969
MonotonicityNot monotonic
2025-09-21T01:35:45.018279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 14539
24.7%
1 13621
23.1%
3 9677
16.4%
5 8524
14.5%
4 4862
 
8.3%
0 3603
 
6.1%
6 1220
 
2.1%
10 936
 
1.6%
7 887
 
1.5%
8 532
 
0.9%
Other values (23) 494
 
0.8%
ValueCountFrequency (%)
0 3603
 
6.1%
1 13621
23.1%
2 14539
24.7%
3 9677
16.4%
4 4862
 
8.3%
5 8524
14.5%
6 1220
 
2.1%
7 887
 
1.5%
8 532
 
0.9%
9 179
 
0.3%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 4
< 0.1%
26 1
 
< 0.1%
25 5
< 0.1%
24 1
 
< 0.1%

adults
Real number (ℝ)

Skewed 

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9691485
Minimum-1
Maximum100
Zeros104
Zeros (%)0.2%
Negative99
Negative (%)0.2%
Memory size460.2 KiB
2025-09-21T01:35:45.134589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum100
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9434539
Coefficient of variation (CV)1.4947851
Kurtosis657.97785
Mean1.9691485
Median Absolute Deviation (MAD)0
Skewness24.816173
Sum115973
Variance8.6639207
MonotonicityNot monotonic
2025-09-21T01:35:45.235030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 45712
77.6%
1 10610
 
18.0%
3 2225
 
3.8%
0 104
 
0.2%
-1 99
 
0.2%
4 34
 
0.1%
66 6
 
< 0.1%
65 5
 
< 0.1%
26 5
 
< 0.1%
69 4
 
< 0.1%
Other values (44) 91
 
0.2%
ValueCountFrequency (%)
-1 99
 
0.2%
0 104
 
0.2%
1 10610
 
18.0%
2 45712
77.6%
3 2225
 
3.8%
4 34
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
100 3
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
95 3
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 4
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
86 2
< 0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size460.2 KiB
0.0
54415 
1.0
 
2335
2.0
 
2114
3.0
 
26
10.0
 
1

Length

Max length4
Median length3
Mean length3.000017
Min length3

Characters and Unicode

Total characters176,674
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 54415
92.4%
1.0 2335
 
4.0%
2.0 2114
 
3.6%
3.0 26
 
< 0.1%
10.0 1
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2025-09-21T01:35:45.333379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:45.420832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 54415
92.4%
1.0 2335
 
4.0%
2.0 2114
 
3.6%
3.0 26
 
< 0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 113307
64.1%
. 58891
33.3%
1 2336
 
1.3%
2 2114
 
1.2%
3 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 113307
64.1%
. 58891
33.3%
1 2336
 
1.3%
2 2114
 
1.2%
3 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 113307
64.1%
. 58891
33.3%
1 2336
 
1.3%
2 2114
 
1.2%
3 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 113307
64.1%
. 58891
33.3%
1 2336
 
1.3%
2 2114
 
1.2%
3 26
 
< 0.1%

babies
Real number (ℝ)

Skewed  Zeros 

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13659903
Minimum-1
Maximum100
Zeros58094
Zeros (%)98.6%
Negative90
Negative (%)0.2%
Memory size460.2 KiB
2025-09-21T01:35:45.512370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1158893
Coefficient of variation (CV)22.810478
Kurtosis665.69282
Mean0.13659903
Median Absolute Deviation (MAD)0
Skewness25.353958
Sum8045
Variance9.7087659
MonotonicityNot monotonic
2025-09-21T01:35:45.664471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 58094
98.6%
1 598
 
1.0%
-1 90
 
0.2%
2 9
 
< 0.1%
51 6
 
< 0.1%
57 5
 
< 0.1%
77 5
 
< 0.1%
73 5
 
< 0.1%
81 4
 
< 0.1%
94 4
 
< 0.1%
Other values (37) 75
 
0.1%
ValueCountFrequency (%)
-1 90
 
0.2%
0 58094
98.6%
1 598
 
1.0%
2 9
 
< 0.1%
10 1
 
< 0.1%
50 1
 
< 0.1%
51 6
 
< 0.1%
52 2
 
< 0.1%
53 2
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
100 2
< 0.1%
99 2
< 0.1%
98 2
< 0.1%
97 4
< 0.1%
96 2
< 0.1%
95 1
 
< 0.1%
94 4
< 0.1%
93 3
< 0.1%
92 3
< 0.1%
91 1
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
BB
45060 
HB
10096 
SC
 
1780
Undefined
 
1169
FB
 
790

Length

Max length9
Median length2
Mean length2.1389422
Min length2

Characters and Unicode

Total characters125,973
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 45060
76.5%
HB 10096
 
17.1%
SC 1780
 
3.0%
Undefined 1169
 
2.0%
FB 790
 
1.3%

Length

2025-09-21T01:35:45.829081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:45.928622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
bb 45060
76.5%
hb 10096
 
17.1%
sc 1780
 
3.0%
undefined 1169
 
2.0%
fb 790
 
1.3%

Most occurring characters

ValueCountFrequency (%)
B 101006
80.2%
H 10096
 
8.0%
n 2338
 
1.9%
d 2338
 
1.9%
e 2338
 
1.9%
S 1780
 
1.4%
C 1780
 
1.4%
U 1169
 
0.9%
f 1169
 
0.9%
i 1169
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 101006
80.2%
H 10096
 
8.0%
n 2338
 
1.9%
d 2338
 
1.9%
e 2338
 
1.9%
S 1780
 
1.4%
C 1780
 
1.4%
U 1169
 
0.9%
f 1169
 
0.9%
i 1169
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 101006
80.2%
H 10096
 
8.0%
n 2338
 
1.9%
d 2338
 
1.9%
e 2338
 
1.9%
S 1780
 
1.4%
C 1780
 
1.4%
U 1169
 
0.9%
f 1169
 
0.9%
i 1169
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 101006
80.2%
H 10096
 
8.0%
n 2338
 
1.9%
d 2338
 
1.9%
e 2338
 
1.9%
S 1780
 
1.4%
C 1780
 
1.4%
U 1169
 
0.9%
f 1169
 
0.9%
i 1169
 
0.9%
Distinct141
Distinct (%)0.2%
Missing478
Missing (%)0.8%
Memory size460.2 KiB
2025-09-21T01:35:46.460593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9861855
Min length2

Characters and Unicode

Total characters174,444
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 27559
47.2%
gbr 7595
 
13.0%
esp 5275
 
9.0%
fra 3037
 
5.2%
irl 2371
 
4.1%
deu 2022
 
3.5%
ita 1290
 
2.2%
cn 807
 
1.4%
nld 748
 
1.3%
bel 733
 
1.3%
Other values (131) 6980
 
11.9%
2025-09-21T01:35:46.736647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 42796
24.5%
P 33418
19.2%
T 29542
16.9%
E 9298
 
5.3%
B 9147
 
5.2%
G 7985
 
4.6%
S 7221
 
4.1%
A 6903
 
4.0%
L 4699
 
2.7%
U 4268
 
2.4%
Other values (16) 19167
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 174444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 42796
24.5%
P 33418
19.2%
T 29542
16.9%
E 9298
 
5.3%
B 9147
 
5.2%
G 7985
 
4.6%
S 7221
 
4.1%
A 6903
 
4.0%
L 4699
 
2.7%
U 4268
 
2.4%
Other values (16) 19167
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 174444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 42796
24.5%
P 33418
19.2%
T 29542
16.9%
E 9298
 
5.3%
B 9147
 
5.2%
G 7985
 
4.6%
S 7221
 
4.1%
A 6903
 
4.0%
L 4699
 
2.7%
U 4268
 
2.4%
Other values (16) 19167
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 174444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 42796
24.5%
P 33418
19.2%
T 29542
16.9%
E 9298
 
5.3%
B 9147
 
5.2%
G 7985
 
4.6%
S 7221
 
4.1%
A 6903
 
4.0%
L 4699
 
2.7%
U 4268
 
2.4%
Other values (16) 19167
11.0%

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
Online TA
25742 
Offline TA/TO
12455 
Groups
10399 
Direct
7400 
Corporate
2621 
Other values (3)
 
278

Length

Max length13
Median length9
Mean length8.9561423
Min length6

Characters and Unicode

Total characters527,472
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 25742
43.7%
Offline TA/TO 12455
21.1%
Groups 10399
17.7%
Direct 7400
 
12.6%
Corporate 2621
 
4.5%
Complementary 254
 
0.4%
Aviation 22
 
< 0.1%
Undefined 2
 
< 0.1%

Length

2025-09-21T01:35:46.846234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:46.946609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
online 25742
26.5%
ta 25742
26.5%
offline 12455
12.8%
ta/to 12455
12.8%
groups 10399
10.7%
direct 7400
 
7.6%
corporate 2621
 
2.7%
complementary 254
 
0.3%
aviation 22
 
< 0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 64219
12.2%
O 50652
9.6%
T 50652
9.6%
e 48730
9.2%
i 45643
8.7%
l 38451
 
7.3%
A 38219
 
7.2%
38197
 
7.2%
f 24912
 
4.7%
r 23295
 
4.4%
Other values (16) 104502
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 527472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 64219
12.2%
O 50652
9.6%
T 50652
9.6%
e 48730
9.2%
i 45643
8.7%
l 38451
 
7.3%
A 38219
 
7.2%
38197
 
7.2%
f 24912
 
4.7%
r 23295
 
4.4%
Other values (16) 104502
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 527472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 64219
12.2%
O 50652
9.6%
T 50652
9.6%
e 48730
9.2%
i 45643
8.7%
l 38451
 
7.3%
A 38219
 
7.2%
38197
 
7.2%
f 24912
 
4.7%
r 23295
 
4.4%
Other values (16) 104502
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 527472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 64219
12.2%
O 50652
9.6%
T 50652
9.6%
e 48730
9.2%
i 45643
8.7%
l 38451
 
7.3%
A 38219
 
7.2%
38197
 
7.2%
f 24912
 
4.7%
r 23295
 
4.4%
Other values (16) 104502
19.8%

distribution_channel
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
TA/TO
46358 
Direct
8841 
Corporate
 
3680
GDS
 
11
Undefined
 
5

Length

Max length9
Median length5
Mean length5.400017
Min length3

Characters and Unicode

Total characters318,034
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 46358
78.7%
Direct 8841
 
15.0%
Corporate 3680
 
6.2%
GDS 11
 
< 0.1%
Undefined 5
 
< 0.1%

Length

2025-09-21T01:35:47.044701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:47.131274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 46358
78.7%
direct 8841
 
15.0%
corporate 3680
 
6.2%
gds 11
 
< 0.1%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 92716
29.2%
/ 46358
14.6%
O 46358
14.6%
A 46358
14.6%
r 16201
 
5.1%
e 12531
 
3.9%
t 12521
 
3.9%
D 8852
 
2.8%
i 8846
 
2.8%
c 8841
 
2.8%
Other values (10) 18452
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 318034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 92716
29.2%
/ 46358
14.6%
O 46358
14.6%
A 46358
14.6%
r 16201
 
5.1%
e 12531
 
3.9%
t 12521
 
3.9%
D 8852
 
2.8%
i 8846
 
2.8%
c 8841
 
2.8%
Other values (10) 18452
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 318034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 92716
29.2%
/ 46358
14.6%
O 46358
14.6%
A 46358
14.6%
r 16201
 
5.1%
e 12531
 
3.9%
t 12521
 
3.9%
D 8852
 
2.8%
i 8846
 
2.8%
c 8841
 
2.8%
Other values (10) 18452
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 318034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 92716
29.2%
/ 46358
14.6%
O 46358
14.6%
A 46358
14.6%
r 16201
 
5.1%
e 12531
 
3.9%
t 12521
 
3.9%
D 8852
 
2.8%
i 8846
 
2.8%
c 8841
 
2.8%
Other values (10) 18452
 
5.8%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
0
57117 
1
 
1778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58,895
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

Length

2025-09-21T01:35:47.214057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:47.291442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57117
97.0%
1 1778
 
3.0%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.069190933
Minimum0
Maximum26
Zeros57800
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:47.353504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1021382
Coefficient of variation (CV)15.928939
Kurtosis458.83288
Mean0.069190933
Median Absolute Deviation (MAD)0
Skewness21.14837
Sum4075
Variance1.2147086
MonotonicityNot monotonic
2025-09-21T01:35:47.418468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 57800
98.1%
1 896
 
1.5%
24 48
 
0.1%
2 44
 
0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
19 19
 
< 0.1%
3 14
 
< 0.1%
14 14
 
< 0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
0 57800
98.1%
1 896
 
1.5%
2 44
 
0.1%
3 14
 
< 0.1%
4 6
 
< 0.1%
5 3
 
< 0.1%
14 14
 
< 0.1%
19 19
 
< 0.1%
24 48
 
0.1%
25 25
 
< 0.1%
ValueCountFrequency (%)
26 26
 
< 0.1%
25 25
 
< 0.1%
24 48
 
0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
5 3
 
< 0.1%
4 6
 
< 0.1%
3 14
 
< 0.1%
2 44
 
0.1%
1 896
1.5%

previous_bookings_not_canceled
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.099617964
Minimum0
Maximum30
Zeros56863
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:47.517314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82916558
Coefficient of variation (CV)8.3234544
Kurtosis359.36435
Mean0.099617964
Median Absolute Deviation (MAD)0
Skewness16.059944
Sum5867
Variance0.68751556
MonotonicityNot monotonic
2025-09-21T01:35:47.603175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 56863
96.5%
1 973
 
1.7%
2 388
 
0.7%
3 204
 
0.3%
4 127
 
0.2%
5 91
 
0.2%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
0.1%
9 24
 
< 0.1%
Other values (21) 99
 
0.2%
ValueCountFrequency (%)
0 56863
96.5%
1 973
 
1.7%
2 388
 
0.7%
3 204
 
0.3%
4 127
 
0.2%
5 91
 
0.2%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
24 2
< 0.1%
23 2
< 0.1%
22 2
< 0.1%
21 2
< 0.1%

reserved_room_type
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
A
39143 
D
9516 
E
5143 
G
 
1649
F
 
1515
Other values (5)
 
1929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58,895
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 39143
66.5%
D 9516
 
16.2%
E 5143
 
8.7%
G 1649
 
2.8%
F 1515
 
2.6%
C 920
 
1.6%
H 601
 
1.0%
B 400
 
0.7%
L 6
 
< 0.1%
P 2
 
< 0.1%

Length

2025-09-21T01:35:47.684688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:47.784736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a 39143
66.5%
d 9516
 
16.2%
e 5143
 
8.7%
g 1649
 
2.8%
f 1515
 
2.6%
c 920
 
1.6%
h 601
 
1.0%
b 400
 
0.7%
l 6
 
< 0.1%
p 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 39143
66.5%
D 9516
 
16.2%
E 5143
 
8.7%
G 1649
 
2.8%
F 1515
 
2.6%
C 920
 
1.6%
H 601
 
1.0%
B 400
 
0.7%
L 6
 
< 0.1%
P 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 39143
66.5%
D 9516
 
16.2%
E 5143
 
8.7%
G 1649
 
2.8%
F 1515
 
2.6%
C 920
 
1.6%
H 601
 
1.0%
B 400
 
0.7%
L 6
 
< 0.1%
P 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 39143
66.5%
D 9516
 
16.2%
E 5143
 
8.7%
G 1649
 
2.8%
F 1515
 
2.6%
C 920
 
1.6%
H 601
 
1.0%
B 400
 
0.7%
L 6
 
< 0.1%
P 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 39143
66.5%
D 9516
 
16.2%
E 5143
 
8.7%
G 1649
 
2.8%
F 1515
 
2.6%
C 920
 
1.6%
H 601
 
1.0%
B 400
 
0.7%
L 6
 
< 0.1%
P 2
 
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
A
31391 
D
13336 
E
5927 
C
 
2225
F
 
2177
Other values (7)
3839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58,895
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 31391
53.3%
D 13336
22.6%
E 5927
 
10.1%
C 2225
 
3.8%
F 2177
 
3.7%
G 1917
 
3.3%
B 821
 
1.4%
H 712
 
1.2%
I 363
 
0.6%
K 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

Length

2025-09-21T01:35:47.919408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 31391
53.3%
d 13336
22.6%
e 5927
 
10.1%
c 2225
 
3.8%
f 2177
 
3.7%
g 1917
 
3.3%
b 821
 
1.4%
h 712
 
1.2%
i 363
 
0.6%
k 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 31391
53.3%
D 13336
22.6%
E 5927
 
10.1%
C 2225
 
3.8%
F 2177
 
3.7%
G 1917
 
3.3%
B 821
 
1.4%
H 712
 
1.2%
I 363
 
0.6%
K 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 31391
53.3%
D 13336
22.6%
E 5927
 
10.1%
C 2225
 
3.8%
F 2177
 
3.7%
G 1917
 
3.3%
B 821
 
1.4%
H 712
 
1.2%
I 363
 
0.6%
K 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 31391
53.3%
D 13336
22.6%
E 5927
 
10.1%
C 2225
 
3.8%
F 2177
 
3.7%
G 1917
 
3.3%
B 821
 
1.4%
H 712
 
1.2%
I 363
 
0.6%
K 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 31391
53.3%
D 13336
22.6%
E 5927
 
10.1%
C 2225
 
3.8%
F 2177
 
3.7%
G 1917
 
3.3%
B 821
 
1.4%
H 712
 
1.2%
I 363
 
0.6%
K 23
 
< 0.1%
Other values (2) 3
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24300874
Minimum0
Maximum20
Zeros49268
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:48.020347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.695205
Coefficient of variation (CV)2.860823
Kurtosis68.966364
Mean0.24300874
Median Absolute Deviation (MAD)0
Skewness5.7511422
Sum14312
Variance0.48331
MonotonicityNot monotonic
2025-09-21T01:35:48.101305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 49268
83.7%
1 6755
 
11.5%
2 1921
 
3.3%
3 553
 
0.9%
4 220
 
0.4%
5 81
 
0.1%
6 42
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
Other values (8) 16
 
< 0.1%
ValueCountFrequency (%)
0 49268
83.7%
1 6755
 
11.5%
2 1921
 
3.3%
3 553
 
0.9%
4 220
 
0.4%
5 81
 
0.1%
6 42
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
10 3
 
< 0.1%
9 7
< 0.1%
8 11
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.2 KiB
No Deposit
52333 
Non Refund
5457 
No Refund
 
962
Refundable
 
143

Length

Max length10
Median length10
Mean length9.9836658
Min length9

Characters and Unicode

Total characters587,988
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 52333
88.9%
Non Refund 5457
 
9.3%
No Refund 962
 
1.6%
Refundable 143
 
0.2%

Length

2025-09-21T01:35:48.201607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:48.285095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 53295
45.3%
deposit 52333
44.5%
refund 6419
 
5.5%
non 5457
 
4.6%
refundable 143
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 111085
18.9%
e 59038
10.0%
N 58752
10.0%
58752
10.0%
s 52333
8.9%
i 52333
8.9%
t 52333
8.9%
p 52333
8.9%
D 52333
8.9%
n 12019
 
2.0%
Other values (7) 26677
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 111085
18.9%
e 59038
10.0%
N 58752
10.0%
58752
10.0%
s 52333
8.9%
i 52333
8.9%
t 52333
8.9%
p 52333
8.9%
D 52333
8.9%
n 12019
 
2.0%
Other values (7) 26677
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 111085
18.9%
e 59038
10.0%
N 58752
10.0%
58752
10.0%
s 52333
8.9%
i 52333
8.9%
t 52333
8.9%
p 52333
8.9%
D 52333
8.9%
n 12019
 
2.0%
Other values (7) 26677
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 111085
18.9%
e 59038
10.0%
N 58752
10.0%
58752
10.0%
s 52333
8.9%
i 52333
8.9%
t 52333
8.9%
p 52333
8.9%
D 52333
8.9%
n 12019
 
2.0%
Other values (7) 26677
 
4.5%

agent
Real number (ℝ)

Missing 

Distinct249
Distinct (%)0.5%
Missing9132
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean146.98308
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:48.368455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median196
Q3240
95-th percentile314
Maximum535
Range534
Interquartile range (IQR)231

Descriptive statistics

Standard deviation120.11499
Coefficient of variation (CV)0.81720282
Kurtosis-1.0837932
Mean146.98308
Median Absolute Deviation (MAD)81
Skewness0.11667503
Sum7314319
Variance14427.61
MonotonicityNot monotonic
2025-09-21T01:35:48.484964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 13907
23.6%
9 6997
11.9%
1 3184
 
5.4%
250 2870
 
4.9%
241 1721
 
2.9%
6 1378
 
2.3%
40 1013
 
1.7%
314 927
 
1.6%
242 779
 
1.3%
37 615
 
1.0%
Other values (239) 16372
27.8%
(Missing) 9132
15.5%
ValueCountFrequency (%)
1 3184
5.4%
2 120
 
0.2%
3 564
 
1.0%
5 256
 
0.4%
6 1378
 
2.3%
7 481
 
0.8%
8 558
 
0.9%
9 6997
11.9%
10 39
 
0.1%
11 225
 
0.4%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 50
0.1%
493 35
0.1%

company
Unsupported

Missing  Rejected  Unsupported 

Missing55416
Missing (%)94.1%
Memory size460.2 KiB

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct99
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.5259789
Minimum0
Maximum391
Zeros56503
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:48.600479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.841676
Coefficient of variation (CV)6.1945001
Kurtosis101.43631
Mean3.5259789
Median Absolute Deviation (MAD)0
Skewness8.9202327
Sum207659
Variance477.05883
MonotonicityNot monotonic
2025-09-21T01:35:48.705588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56503
95.9%
39 186
 
0.3%
58 164
 
0.3%
31 102
 
0.2%
69 89
 
0.2%
87 80
 
0.1%
63 80
 
0.1%
111 70
 
0.1%
101 65
 
0.1%
77 63
 
0.1%
Other values (89) 1492
 
2.5%
ValueCountFrequency (%)
0 56503
95.9%
1 7
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.1%
4 10
 
< 0.1%
5 4
 
< 0.1%
6 4
 
< 0.1%
8 6
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
391 15
 
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
0.1%
224 10
 
< 0.1%
223 60
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
187 45
0.1%

customer_type
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing589
Missing (%)1.0%
Memory size460.2 KiB
Transient
42430 
Transient-Party
13078 
Contract
 
2486
Group
 
312

Length

Max length15
Median length9
Mean length10.281755
Min length5

Characters and Unicode

Total characters599,488
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 42430
72.0%
Transient-Party 13078
 
22.2%
Contract 2486
 
4.2%
Group 312
 
0.5%
(Missing) 589
 
1.0%

Length

2025-09-21T01:35:48.801909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:48.885848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
transient 42430
72.8%
transient-party 13078
 
22.4%
contract 2486
 
4.3%
group 312
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 113502
18.9%
t 73558
12.3%
r 71384
11.9%
a 71072
11.9%
T 55508
9.3%
s 55508
9.3%
i 55508
9.3%
e 55508
9.3%
y 13078
 
2.2%
- 13078
 
2.2%
Other values (7) 21784
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 599488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 113502
18.9%
t 73558
12.3%
r 71384
11.9%
a 71072
11.9%
T 55508
9.3%
s 55508
9.3%
i 55508
9.3%
e 55508
9.3%
y 13078
 
2.2%
- 13078
 
2.2%
Other values (7) 21784
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 599488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 113502
18.9%
t 73558
12.3%
r 71384
11.9%
a 71072
11.9%
T 55508
9.3%
s 55508
9.3%
i 55508
9.3%
e 55508
9.3%
y 13078
 
2.2%
- 13078
 
2.2%
Other values (7) 21784
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 599488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 113502
18.9%
t 73558
12.3%
r 71384
11.9%
a 71072
11.9%
T 55508
9.3%
s 55508
9.3%
i 55508
9.3%
e 55508
9.3%
y 13078
 
2.2%
- 13078
 
2.2%
Other values (7) 21784
 
3.6%

adr
Real number (ℝ)

Zeros 

Distinct6769
Distinct (%)11.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean96.250426
Minimum-6.38
Maximum5400
Zeros954
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size460.2 KiB
2025-09-21T01:35:48.986162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile34.02
Q160
median84
Q3120
95-th percentile207.618
Maximum5400
Range5406.38
Interquartile range (IQR)60

Descriptive statistics

Standard deviation58.555599
Coefficient of variation (CV)0.60836716
Kurtosis1143.6777
Mean96.250426
Median Absolute Deviation (MAD)29
Skewness13.602849
Sum5668572.6
Variance3428.7582
MonotonicityNot monotonic
2025-09-21T01:35:49.086617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 1786
 
3.0%
75 1077
 
1.8%
48 1037
 
1.8%
0 954
 
1.6%
65 940
 
1.6%
60 803
 
1.4%
90 734
 
1.2%
120 703
 
1.2%
80 701
 
1.2%
70 663
 
1.1%
Other values (6759) 49496
84.0%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 954
1.6%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 3
 
< 0.1%
1.56 2
 
< 0.1%
1.8 1
 
< 0.1%
2 8
 
< 0.1%
2.4 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
508 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
388 2
< 0.1%
387 1
< 0.1%

required_car_parking_spaces
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing589
Missing (%)1.0%
Memory size460.2 KiB
0.0
52709 
1.0
5569 
2.0
 
25
8.0
 
2
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174,918
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 52709
89.5%
1.0 5569
 
9.5%
2.0 25
 
< 0.1%
8.0 2
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 589
 
1.0%

Length

2025-09-21T01:35:49.184681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:49.268216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 52709
90.4%
1.0 5569
 
9.6%
2.0 25
 
< 0.1%
8.0 2
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111015
63.5%
. 58306
33.3%
1 5569
 
3.2%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 174918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111015
63.5%
. 58306
33.3%
1 5569
 
3.2%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 174918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111015
63.5%
. 58306
33.3%
1 5569
 
3.2%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 174918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111015
63.5%
. 58306
33.3%
1 5569
 
3.2%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.51222535
Minimum0
Maximum5
Zeros37158
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size460.2 KiB
2025-09-21T01:35:49.351288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7683779
Coefficient of variation (CV)1.5000778
Kurtosis1.8884772
Mean0.51222535
Median Absolute Deviation (MAD)0
Skewness1.4897608
Sum30167
Variance0.59040459
MonotonicityNot monotonic
2025-09-21T01:35:49.419032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 37158
63.1%
1 14711
 
25.0%
2 5796
 
9.8%
3 1066
 
1.8%
4 149
 
0.3%
5 14
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 37158
63.1%
1 14711
 
25.0%
2 5796
 
9.8%
3 1066
 
1.8%
4 149
 
0.3%
5 14
 
< 0.1%
ValueCountFrequency (%)
5 14
 
< 0.1%
4 149
 
0.3%
3 1066
 
1.8%
2 5796
 
9.8%
1 14711
 
25.0%
0 37158
63.1%

reservation_status
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing589
Missing (%)1.0%
Memory size460.2 KiB
Check-Out
34291 
Canceled
23218 
No-Show
 
797

Length

Max length9
Median length9
Mean length8.574452
Min length7

Characters and Unicode

Total characters499,942
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 34291
58.2%
Canceled 23218
39.4%
No-Show 797
 
1.4%
(Missing) 589
 
1.0%

Length

2025-09-21T01:35:49.503882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-21T01:35:49.609798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
check-out 34291
58.8%
canceled 23218
39.8%
no-show 797
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 80727
16.1%
C 57509
11.5%
c 57509
11.5%
h 35088
7.0%
- 35088
7.0%
u 34291
6.9%
t 34291
6.9%
O 34291
6.9%
k 34291
6.9%
a 23218
 
4.6%
Other values (7) 73639
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 80727
16.1%
C 57509
11.5%
c 57509
11.5%
h 35088
7.0%
- 35088
7.0%
u 34291
6.9%
t 34291
6.9%
O 34291
6.9%
k 34291
6.9%
a 23218
 
4.6%
Other values (7) 73639
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 80727
16.1%
C 57509
11.5%
c 57509
11.5%
h 35088
7.0%
- 35088
7.0%
u 34291
6.9%
t 34291
6.9%
O 34291
6.9%
k 34291
6.9%
a 23218
 
4.6%
Other values (7) 73639
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 80727
16.1%
C 57509
11.5%
c 57509
11.5%
h 35088
7.0%
- 35088
7.0%
u 34291
6.9%
t 34291
6.9%
O 34291
6.9%
k 34291
6.9%
a 23218
 
4.6%
Other values (7) 73639
14.7%
Distinct921
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Memory size460.2 KiB
Minimum2014-11-18 00:00:00
Maximum2017-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-21T01:35:49.710770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:49.818080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

kids
Real number (ℝ)

Missing 

Distinct41
Distinct (%)20.4%
Missing58694
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean34.41791
Minimum-1
Maximum100
Zeros0
Zeros (%)0.0%
Negative105
Negative (%)0.2%
Memory size460.2 KiB
2025-09-21T01:35:49.926467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q365
95-th percentile96
Maximum100
Range101
Interquartile range (IQR)66

Descriptive statistics

Standard deviation38.571032
Coefficient of variation (CV)1.1206674
Kurtosis-1.6413267
Mean34.41791
Median Absolute Deviation (MAD)0
Skewness0.31316079
Sum6918
Variance1487.7245
MonotonicityNot monotonic
2025-09-21T01:35:50.018777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
-1 105
 
0.2%
62 6
 
< 0.1%
60 5
 
< 0.1%
63 5
 
< 0.1%
58 5
 
< 0.1%
57 5
 
< 0.1%
100 3
 
< 0.1%
80 3
 
< 0.1%
98 3
 
< 0.1%
59 3
 
< 0.1%
Other values (31) 58
 
0.1%
(Missing) 58694
99.7%
ValueCountFrequency (%)
-1 105
0.2%
52 2
 
< 0.1%
53 3
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 2
 
< 0.1%
57 5
 
< 0.1%
58 5
 
< 0.1%
59 3
 
< 0.1%
60 5
 
< 0.1%
ValueCountFrequency (%)
100 3
< 0.1%
98 3
< 0.1%
97 3
< 0.1%
96 3
< 0.1%
95 3
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
89 2
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%

Interactions

2025-09-21T01:35:40.710780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.256195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.857505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.614904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.155894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.815332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.339396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.839912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.429938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.929395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.485465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.230490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.796832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.445821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.973175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.785333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.374981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.951627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.717446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.271414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.915909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.438506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.111022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.530500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.030625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.601748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.374896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.904389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.548748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.335008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.862329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.489525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.055610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.814999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.372959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.011462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.528587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.204579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.626460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.130189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.703519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.480427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.006944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.639896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.444258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.943345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.635588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.151678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.914567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.475895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.109239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.628892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.300757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.724007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.236258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.806151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.579062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.111521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.740368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.543782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.039149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.751298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.267689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.017985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.584463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.207415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.733187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.400358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.869816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.341796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.118358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.678622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.218084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.851535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.648370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.169784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.852222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.351511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.113548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.684032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.332294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.823628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.491942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.984863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.438589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.223868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.778157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.319640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.941061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.744201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.254993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:18.954501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.567848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.210061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.782601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.457314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.916813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.587391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.076430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.536560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.324074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.876760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.429743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.041201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.839195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.332030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.055023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.671860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.307106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.024040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.556784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.019120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.677838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.170811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.618232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.423806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.011281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.555617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.135171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:39.936605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.401345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.155912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.807965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.407122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.124599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.653270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.101582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.771351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.264992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.719604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.526295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.121385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.690806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.218909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.034178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.487729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.262465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:20.968950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.513721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.226420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.751070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.201828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.868729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.362317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.819567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.628456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.218916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.813027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.318925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.133720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.567118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.367890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.088856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.619293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.331598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.855125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.301509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:28.970928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.468612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:31.935034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.740752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.320468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:36.929369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.435435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.238283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.634970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.471569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.188629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.714858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.431152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:25.955160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.411728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.062848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.563759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.073923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.840821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.416027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.030921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.520638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.332846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.718103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.568077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.306449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.827284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.537719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.051726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.531724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.164374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.667295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.207310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:33.948922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.524457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.146014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.639031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.438711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.802325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.668496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.431969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:22.922297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.638260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.156374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.661786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.258660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.759836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.308008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.049442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.625018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.250551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.718149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.534585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:41.888615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:19.767898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:21.530738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:23.017833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:24.728857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:26.234981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:27.755314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:29.346328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:30.845387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:32.400201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:34.146016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:35.714298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:37.354754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:38.875576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-09-21T01:35:40.627816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2025-09-21T01:35:42.051522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-21T01:35:42.701550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-21T01:35:43.184924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datekids
0Resort Hotel03422015.0July2710020.00BBPRTDirectDirect000CC3No DepositNaNNaN0.0Transient0.00.00.0Check-Out2015-07-01NaN
1Resort Hotel07372015.0July2710020.00BBPRTDirectDirect000CC4No DepositNaNNaN0.0Transient0.00.00.0Check-Out2015-07-01NaN
2Resort Hotel072015.0July2710110.00BBGBRDirectDirect000AC0No DepositNaNNaN0.0Transient75.00.00.0Check-Out2015-07-02NaN
3Resort Hotel0132015.0July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.0NaN0.0Transient75.00.00.0Check-Out2015-07-02NaN
4Resort Hotel0142015.0July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0.0Transient98.00.01.0Check-Out2015-07-03NaN
5Resort Hotel0142015.0July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0.0Transient98.00.01.0Check-Out2015-07-03NaN
6Resort Hotel002015.0July2710220.00BBPRTDirectDirect000CC0No DepositNaNNaN0.0Transient107.00.00.0Check-Out2015-07-03NaN
7Resort Hotel092015.0July2710220.00FBPRTDirectDirect000CC0No Deposit303.0NaN0.0Transient103.00.01.0Check-Out2015-07-03NaN
8Resort Hotel1852015.0July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0.0Transient82.00.01.0Canceled2015-05-06NaN
9Resort Hotel1752015.0July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.0NaN0.0Transient105.50.00.0Canceled2015-04-22NaN
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datekids
58885City Hotel16052016.0October43171220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0.0Transient60.000.00.0Canceled2016-09-20NaN
58886City Hotel16052016.0October43171220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0.0Transient60.000.00.0Canceled2016-09-20NaN
58887City Hotel16052016.0October43171220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0.0Transient60.000.00.0Canceled2016-09-20NaN
58888City Hotel16052016.0October43171220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0.0Transient60.000.00.0Canceled2016-09-20NaN
58889City Hotel16052016.0October43171220.00BBPRTGroupsTA/TO000AA0No Refund1.0NUNaNNaNNaNNaNNaNNaNNaNNaN
58890Resort Hotel032016.0April16111010.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0.0Transient-Party56.000.01.0Check-Out2016-04-12NaN
58891Resort Hotel11582016.0May2082220.00BBPRTDirectDirect000FF2No Deposit250.0NaN0.0Transient83.050.01.0Canceled2016-01-21NaN
58892City Hotel1182016.0August3262220.00BBESPOnline TATA/TO000AA0No Deposit9.0NaN0.0Transient151.000.02.0Canceled2016-07-28NaN
58893Resort Hotel13832016.0October4161320.00BBPRTGroupsTA/TO000AA0No Deposit315.0NaN0.0Transient-Party48.000.00.0Canceled2016-03-04NaN
58894City Hotel11852016.0July2850420.00BBDEUOnline TATA/TO000AA0No Deposit9.0NaN0.0Transient90.950.01.0Canceled2016-05-31NaN

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datekids# duplicates
1309City Hotel11882016.0June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.039.0Transient130.00.00.0Canceled2016-01-18NaN91
1219City Hotel11582016.0May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.031.0Transient130.00.00.0Canceled2016-01-18NaN77
733City Hotel1372016.0October42130320.00BBPRTOffline TA/TOTA/TO000AA0No Deposit56.00.0Transient-Party105.00.00.0Canceled2016-09-06NaN75
740City Hotel1392015.0August33140220.00HBPRTOffline TA/TOTA/TO000AA0No Deposit6.00.0Transient-Party101.50.00.0Canceled2015-07-06NaN68
886City Hotel1712016.0June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00.0Transient120.00.00.0Canceled2016-04-27NaN68
550City Hotel112016.0February10282110.00BBPRTOffline TA/TOTA/TO000AA0No Deposit134.00.0Transient-Party60.00.00.0Canceled2016-02-27NaN63
960City Hotel1872015.0September39252320.00BBPRTGroupsTA/TO000AA0Non Refund1.00.0Transient170.00.00.0Canceled2015-09-09NaN59
812City Hotel1562016.0June2480120.00BBPRTOffline TA/TOCorporate000AA0No Deposit191.00.0Transient-Party120.00.00.0Canceled2016-06-02NaN55
905City Hotel1742015.0September38180220.00HBPRTOffline TA/TOTA/TO000AA0Non Refund6.00.0Transient-Party101.50.00.0Canceled2015-07-06NaN54
1055City Hotel11052016.0April1560120.00BBPRTOffline TA/TOTA/TO000AA0Non Refund12.00.0Transient75.00.00.0Canceled2016-01-18NaN52